Genetic and artificial bee colony algorithms for scheduling of multi-skilled manpower in combined manpower-vehicle routing problem

Abstract

This paper investigates a scheduling combined manpower-vehicle routing problem with a central depot in and a set of multi-skilled manpower for serving to customers. Teams are in different range of competencies that it will affect the service time duration. Vehicles are in different moving speeds and costs and not all the vehicles are capable to move toward all the customers' sites. The objective is to minimize the total cost of servicing, routing, and lateness penalties. This paper presents a mixed integer programming model and two meta-heuristic approaches of genetic algorithm (GA) and artificial bee colony algorithm (ABC) are developed to solve the generated problems. Furthermore, Taguchi experimental design method is applied to set the proper values of parameters. The available results show the higher performance of proposed GA compared with ABC, in quality of solutions.

COVID-19 Vaccines distribution routes with bioinspired metaheuristic algorithms: Resoluteness

“…The performance of the metaheuristics is considerably affected by the parameter configuration because it controls some characteristics such as convergence, quality of the solution and execution time. Each algorithm was executed with different parameter configurations based on recommended ranges from the literature [28], [35], [36], [37], [38], [39], [40], [41], followed by an iterative process of adjustment and refinement. Specifically, for each algorithm, we started with standard or recommended parameter values, followed by a sensitivity and performance analysis where each parameter was adjusted independently while keeping the others constant across multiple executions.…”